A Novel Low-Rank Minimization Approach For Clustering Large-Scale Health Data Using A Novel Kernel Ridge Regression Model – We propose a new approach for supervised clustering, where a cluster of nodes is sampled from a random distribution, and a low probability distribution is modeled. The low probability distribution is the subset of the sample which contains all nodes that are sampled from the distribution. An efficient low-rank projection procedure is proposed for this problem. In particular, the projection is formulated as a sub-weight function for the high-dimensional feature representation, which is then used to construct a sparse projection. We first show that the sparse projection is a regularizer for this problem, which, in turn, allows to automatically handle outliers. Second, we show how we can use high-dimensional features represented by such sparse projections to estimate high-dimensional features corresponding to high-dimensional data. Third, we show some practical applications using our approach. We report the proposed process and some results of the implementation of the method for clustering patients with diabetes.
In this work, we propose a new framework for learning deep CNNs from raw image patches. As a case study, we propose a novel and scalable method for learning deep CNNs using compressed convolutional neural networks (convNNs). We first show that constrained CNNs achieve state-of-the-art performance in many tasks, while using a compact representation of the image patches. We then show that conv nets can be trained to generalize to unseen patches easily. Our experiments show that our deep CNN approach is able to achieve state-of-the-art performance on several benchmark datasets, as compared to other state-of-the-art methods.
A Feature Based Deep Learning Recognition System For Indoor Action Recognition
Using the G-CNNs as Convolutional Networks: Learning to Match with Recurrent Neural Networks
A Novel Low-Rank Minimization Approach For Clustering Large-Scale Health Data Using A Novel Kernel Ridge Regression Model
Fast Convolutional Neural Networks via Nonconvex Kernel NormalizationIn this work, we propose a new framework for learning deep CNNs from raw image patches. As a case study, we propose a novel and scalable method for learning deep CNNs using compressed convolutional neural networks (convNNs). We first show that constrained CNNs achieve state-of-the-art performance in many tasks, while using a compact representation of the image patches. We then show that conv nets can be trained to generalize to unseen patches easily. Our experiments show that our deep CNN approach is able to achieve state-of-the-art performance on several benchmark datasets, as compared to other state-of-the-art methods.